// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once

template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
    return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
                                                 ck_tile::tensor_layout::gemm::RowMajor>>{};
}

template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
                         const ck_tile::index_t kbatch,
                         const float max_accumulated_value)
{
    using ComputeType =
        std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
    // Calculate thresholds
    const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
        ck_tile::integer_divide_ceil(K, kbatch));
    const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
        max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
    // Calculate error due to split_k accumulation
    const auto rtol_split_k =
        ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
    const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
        max_accumulated_value, kbatch);
    // Use higher threshold
    return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}

template <typename Tensor,
          typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CDataType,
          typename ALayout,
          typename BLayout,
          typename CLayout>
void permute_tensor_b(Tensor& tensor)
{
    using GemmShape = ck_tile::TileGemmShape<
        ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
        ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
        ck_tile::
            sequence<GemmConfig::M_Warp_Tile, GemmConfig::N_Warp_Tile, GemmConfig::K_Warp_Tile>,
        GemmConfig::PermuteA,
        GemmConfig::PermuteB>;

    using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<GemmConfig::kPadM,
                                                                 GemmConfig::kPadN,
                                                                 GemmConfig::kPadK,
                                                                 GemmConfig::DoubleSmemBuffer,
                                                                 ALayout,
                                                                 BLayout,
                                                                 CLayout,
                                                                 GemmConfig::TransposeC,
                                                                 GemmConfig::UseStructuredSparsity>;

    using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
                                                                       BDataType,
                                                                       AccDataType,
                                                                       GemmShape,
                                                                       GemmUniversalTraits,
                                                                       GEMM_PIPELINE_SCHEDULER,
                                                                       true,
                                                                       ck_tile::TailNumber::Full>;

    using GemmPipeline = GEMM_PIPELINE<UniversalGemmProblem>;

    const ck_tile::index_t K  = tensor.get_length(0);
    const ck_tile::index_t N  = tensor.get_length(1);
    const ck_tile::index_t K1 = GemmPipeline::GetSmemPackB();
    const ck_tile::index_t K0 = K / K1;

    Tensor tensor_copy = tensor;

    // int K0, N, K1
    for(int j = 0; j < K0; j++)
    {
        for(int i = 0; i < N; i++)
        {
            for(int jj = 0; jj < K1; jj++)
            {
                tensor(j * N * K1 + i * K1 + jj) = tensor_copy(i * K + (j * K1 + jj));
            }
        }
    }
}

template <typename Tensor>
void permute_vectors_i4x4_b(Tensor& tensor)
{
    const ck_tile::index_t K = tensor.get_length(0);
    const ck_tile::index_t N = tensor.get_length(1);
    // vector pk_i4x4 permute
    for(int i = 0; i < N; i++)
    {
        for(int j = 0; j < K; j += 8)
        {
            int8_t input[8];

            for(int k = 0; k < 4; k++)
            {
                int8_t i4x2      = tensor(j + k * 2, i).data;
                input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
                input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
            }

            // permute 01234567->20643175
            {
                int8_t hi   = input[2];
                int8_t lo   = input[0];
                int8_t i4x2 = (hi << 4) | lo;

                tensor(j + 0, i) = i4x2;
            }

            {
                int8_t hi   = input[6];
                int8_t lo   = input[4];
                int8_t i4x2 = (hi << 4) | lo;

                tensor(j + 2, i) = i4x2;
            }

            {
                int8_t hi   = input[3];
                int8_t lo   = input[1];
                int8_t i4x2 = (hi << 4) | lo;

                tensor(j + 4, i) = i4x2;
            }

            {
                int8_t hi   = input[7];
                int8_t lo   = input[5];
                int8_t i4x2 = (hi << 4) | lo;

                tensor(j + 6, i) = i4x2;
            }
        }
    }
}

template <typename ADataType,
          typename BDataType,
          typename AccDataType,
          typename CDataType,
          typename ALayout,
          typename BLayout,
          typename CLayout>
float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
                  ck_tile::DeviceMem& b_k_n_dev_buf,
                  ck_tile::DeviceMem& c_m_n_dev_buf,
                  ck_tile::index_t M,
                  ck_tile::index_t N,
                  ck_tile::index_t K,
                  ck_tile::index_t stride_A,
                  ck_tile::index_t stride_B,
                  ck_tile::index_t stride_C,
                  ck_tile::index_t kbatch,
                  int n_warmup,
                  int n_repeat)
{
    ck_tile::GemmHostArgs args;
    args.a_ptr    = a_m_k_dev_buf.GetDeviceBuffer();
    args.b_ptr    = b_k_n_dev_buf.GetDeviceBuffer();
    args.c_ptr    = c_m_n_dev_buf.GetDeviceBuffer();
    args.k_batch  = kbatch;
    args.M        = M;
    args.N        = N;
    args.K        = K;
    args.stride_A = stride_A;
    args.stride_B = stride_B;
    args.stride_C = stride_C;

    float ave_time =
        gemm_calc<ADataType, BDataType, AccDataType, CDataType, ALayout, BLayout, CLayout>(
            args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});

    std::size_t flop = std::size_t(2) * M * N * K;
    std::size_t num_byte =
        sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
    float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
    float gb_per_sec = num_byte / 1.E6 / ave_time;

    std::cout << "Run Gemm kernel with M=" << M << " N=" << N << " K=" << K
              << " StrideA=" << stride_A << " StrideB=" << stride_B << " StrideC=" << stride_C
              << " A_Layout=" << ALayout::name << " B_Layout =" << BLayout::name
              << " C_Layout=" << CLayout::name << " A_Type=" << DataTypeTraits<ADataType>::name
              << " B_Type=" << DataTypeTraits<BDataType>::name
              << " C_Type=" << DataTypeTraits<CDataType>::name
              << " StructuredSparsity=" << (GemmConfig::UseStructuredSparsity ? "on" : "off")
              << " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << std::endl;

    return ave_time;
}

template <typename ADataType,
          typename BDataType = ADataType,
          typename CDataType = ADataType,
          typename ALayout,
          typename BLayout,
          typename CLayout>
int run_gemm_example_with_layouts(int argc,
                                  char* argv[],
                                  const ALayout a_layout                  = ALayout{},
                                  const BLayout b_layout                  = BLayout{},
                                  [[maybe_unused]] const CLayout c_layout = CLayout{})
{
    auto [result, arg_parser] = create_args(argc, argv);
    if(!result)
        return -1;

    using AccDataType = typename GemmTypeConfig<ADataType, BDataType, CDataType>::AccDataType;

    ck_tile::index_t M = arg_parser.get_int("m");
    ck_tile::index_t N = arg_parser.get_int("n");
    ck_tile::index_t K = arg_parser.get_int("k");

    ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
    ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
    ck_tile::index_t stride_C = arg_parser.get_int("stride_c");

    ck_tile::index_t kbatch      = arg_parser.get_int("split_k");
    int n_warmup                 = arg_parser.get_int("warmup");
    int n_repeat                 = arg_parser.get_int("repeat");
    ck_tile::index_t init_method = arg_parser.get_int("init");

    stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
    stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
    stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));

    ck_tile::HostTensor<ADataType> a_m_k(
        ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
    ck_tile::HostTensor<BDataType> b_k_n(
        ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
    ck_tile::HostTensor<CDataType> c_m_n_dev_result(
        ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));

    if(init_method == 0)
    {
        ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
        ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
    }
    else if(init_method == 1)
    {
        ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
        ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
    }
    else if(init_method == 2)
    {
        ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k);
        ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n);
    }
    else
    {
        a_m_k.SetZero();
        b_k_n.SetZero();
    }

    if(GemmConfig::UseStructuredSparsity)
    {
        ck_tile::AdjustToStructuredSparsity<ADataType>{}(a_m_k);
    }

    ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
    ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
    ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());

    static_assert(!GemmConfig::PermuteA, "Not implemented");
    if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
    {
        // Permute vector pk_i4x4 data for device implementation
        ck_tile::HostTensor<BDataType> b_k_n_dev = b_k_n;
        if constexpr(GemmConfig::PermuteB)
        {
            permute_tensor_b<decltype(b_k_n_dev),
                             ADataType,
                             BDataType,
                             AccDataType,
                             CDataType,
                             ALayout,
                             BLayout,
                             CLayout>(b_k_n_dev);
        }
        permute_vectors_i4x4_b(b_k_n_dev);
        b_k_n_dev_buf.ToDevice(b_k_n_dev.data());
    }
    else
    {
        if constexpr(GemmConfig::PermuteB)
        {
            std::cout << "Permute for this DataType is not implemented." << std::endl;
            return false;
        }
        b_k_n_dev_buf.ToDevice(b_k_n.data());
    }

    a_m_k_dev_buf.ToDevice(a_m_k.data());
    c_m_n_dev_buf.SetZero();
    c_m_n_dev_result.SetZero();

    invoke_gemm<ADataType, BDataType, AccDataType, CDataType, ALayout, BLayout, CLayout>(
        a_m_k_dev_buf,
        b_k_n_dev_buf,
        c_m_n_dev_buf,
        M,
        N,
        K,
        stride_A,
        stride_B,
        stride_C,
        kbatch,
        n_warmup,
        n_repeat);

    c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
    bool pass = true;

    if(arg_parser.get_int("v") == 1)
    {
        ck_tile::HostTensor<CDataType> c_m_n_host_ref(
            ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
        c_m_n_host_ref.SetZero();

        ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
            a_m_k, b_k_n, c_m_n_host_ref);
        const float max_accumulated_value =
            *std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
        const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
            K, kbatch, max_accumulated_value);
        pass = ck_tile::check_err(c_m_n_dev_result,
                                  c_m_n_host_ref,
                                  "Error: Incorrect results!",
                                  rtol_atol.at(ck_tile::number<0>{}),
                                  rtol_atol.at(ck_tile::number<1>{}));

        std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
                  << " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
                  << std::endl;
        std::cout << "The CPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
    }
    else if(arg_parser.get_int("v") == 2)
    {
        if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
        {
            // Restore input for B for gpu reference
            b_k_n_dev_buf.ToDevice(b_k_n.data());
        }
        ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
            ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
        ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
        c_m_n_gpu_ref.SetZero();
        c_m_n_gpu_buf_ref.SetZero();

        ADataType* d_A;
        BDataType* d_B;
        CDataType* d_C;

        ck_tile::hip_check_error(hipMalloc(&d_A, a_m_k.get_element_space_size_in_bytes()));
        ck_tile::hip_check_error(hipMalloc(&d_B, b_k_n.get_element_space_size_in_bytes()));
        ck_tile::hip_check_error(
            hipMalloc(&d_C, c_m_n_dev_result.get_element_space_size_in_bytes()));

        ck_tile::hip_check_error(hipMemcpy(d_A,
                                           a_m_k_dev_buf.GetDeviceBuffer(),
                                           a_m_k.get_element_space_size_in_bytes(),
                                           hipMemcpyHostToDevice));
        ck_tile::hip_check_error(hipMemcpy(d_B,
                                           b_k_n_dev_buf.GetDeviceBuffer(),
                                           b_k_n.get_element_space_size_in_bytes(),
                                           hipMemcpyHostToDevice));

        ck_tile::reference_gemm_gpu<ADataType,
                                    BDataType,
                                    AccDataType,
                                    CDataType,
                                    ALayout,
                                    BLayout,
                                    CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);

        ck_tile::hip_check_error(hipMemcpy(c_m_n_gpu_buf_ref.GetDeviceBuffer(),
                                           d_C,
                                           c_m_n_dev_result.get_element_space_size_in_bytes(),
                                           hipMemcpyDeviceToHost));

        ck_tile::hip_check_error(hipFree(d_A));
        ck_tile::hip_check_error(hipFree(d_B));
        ck_tile::hip_check_error(hipFree(d_C));

        c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
        const float max_accumulated_value =
            *std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
        const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
            K, kbatch, max_accumulated_value);
        pass = ck_tile::check_err(c_m_n_dev_result,
                                  c_m_n_gpu_ref,
                                  "Error: Incorrect results!",
                                  rtol_atol.at(ck_tile::number<0>{}),
                                  rtol_atol.at(ck_tile::number<1>{}));
        std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
                  << " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
                  << std::endl;
        std::cout << "The GPU verification result is: " << (pass ? "correct" : "fail") << std::endl;
    }

    return pass;
}
